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All Journal Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi International Journal of Research in Community Services International Journal of Quantitative Research and Modeling Jurnal Pengabdian Masyarakat Tapis Berseri SisInfo : Jurnal Sistem Informasi dan Informatika
Nursyanti, Reni
Fakultas Teknologi Dan Informatika, Universitas Informatika Dan Bisnis Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Physics Social Sciences Other

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Data Mining Implementation Using Naïve Bayes Algorithm and Decision Tree J48 In Determining Concentration Selection Budiman Budiman; Reni Nursyanti; R Yadi Rakhman Alamsyah; Imannudin Akbar
International Journal of Quantitative Research and Modeling Vol. 1 No. 3 (2020): International Journal of Quantitative Research and Modeling
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v1i3.72

Abstract

Computerization of society has substantially improved the ability to generate and collect data from a variety of sources. A large amount of data has flooded almost every aspect of people's lives. AMIK HASS Bandung has an Informatic Management Study Program consisting of three areas of concentration that can be selected by students in the fourth semester including Computerized Accounting, Computer Administration, and Multimedia. The determination of concentration selection should be precise based on past data, so the academic section must have a pattern or rule to predict concentration selection. In this work, the data mining techniques were using Naive Bayes and Decision Tree J48 using WEKA tools. The data set used in this study was 111 with a split test percentage mode of 75% used as training data as the model formation and 25% as test data to be tested against both models that had been established. The highest accuracy result obtained on Naive Bayes which is obtaining a 71.4% score consisting of 20 instances that were properly clarified from 28 training data. While Decision Tree J48 has a lower accuracy of 64.3% consisting of 18 instances that are properly clarified from 28 training data. In Decision Tree J48 there are 4 patterns or rules formed to determine concentration selection so that the academic section can assist students in determining concentration selection.
Kolaborasi Kreatif Manusia dan Ai Untuk Generasi Masa Depan R. Yadi Rakhman Alamsyah; Reni Nursyanti; Anggi Dewi Nurcahyani
Jurnal Pengabdian Masyarakat Tapis Berseri (JPMTB) Vol. 5 No. 1 (2026): Jurnal Pengabdian Masyarakat Tapis Berseri (JPMTB) (Edition April)
Publisher : Pusat Studi Teknologi Informasi Fakultas Ilmu Komputer Universitas Bandar Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36448/jpmtb.v5i1.181

Abstract

Digital transformation is currently dominated by developments in Artificial Intelligence (AI), which are bringing fundamental changes to the creative industry. For today’s youth, particularly vocational high school (SMK) students, mastering AI is no longer just an option but a necessity to maintain relevance and competence in the future.This Community Service Program (PkM) aims to equip students of SMK Bakti Nusantara 666 with a deep understanding of the creative collaboration between humans and AI. Through methods including counseling, practical mentoring in prompt engineering techniques, and evaluations via pre-tests and post-tests, this program targets an 80% increase in participants' digital literacy.The primary focus of this activity is to position AI as an 'intelligent assistant' that expands the imagination without eliminating the originality of human ideas, while consistently upholding ethics and copyright. The results of this program are expected to produce digital talents who are innovative, responsible, and ready to contribute to the creative economy.
Co-Authors Akbar, Imannudin Aldi Rohman Anggi Dewi Nurcahyani Asep Ramdani Budiman Cecep Kurniawan Chairul Habibi Gagas Langgeng Dwi Prastyo Graha Prakarsa Luthfi Fadle Rynaldy Muhammad Syarif Hartawan Nasution, Vani Maharani R. Yadi Rakhman R. Yadi Rakhman Alamsyah R.Yadi Rakhman Alamsyah Sardjono Sardjono Surya Perdana